Best Advanced Imaging Techniques for Professionals

This $2M machine saves many of the 15M+ lives affected by stroke every year. You lose 2M neurons/min during a stroke and have ~4.5hrs to live. New Computed tomography (CT) perfusion tech extends that window to 24hrs. Yet we know so little about these life saving devices.. The hardware. A rotating X-ray tube spun at 10,000 RPM shoots high-energy beams through your brain while iodine drip flows in your vessels. 128-640 rows of scintillator detectors capture X-rays every few microseconds. The scan takes 30-60s. Each scan generates >100GB raw data. Custom ASICs & GPU clusters process this in real-time, handling 10^9 data points. The image reconstruction pipeline in C++/CUDA uses deconvolution algos to convert X-ray attenuation data into high-def blood flow maps. Takes < 2min. Companies like Rapid AI & Viz ai revolutionized interpretation. Their deep learning systems analyze perfusion maps in minutes, automatically alerting stroke teams. What took experts hours can now happen fast enough to save critical brain tissue. Takes 2-3mins. The entire process, from door to completed scan is done in 15-20mins. Four giants dominate the space — Siemens' SOMATOM Force claims best speed — GE Revolution claims best AI — Canon Aquilion claims widest coverage — Philips claims unique spectral imaging Two trials changed everything in 2018. DAWN showed 49% good outcomes vs 13% control up to 24hrs after stroke. DEFUSE 3 proved similar results up to 16hrs. Both used CT Perfusion to find salvageable tissue, revolutionizing the "time is brain" paradigm. Before, doctors just used time (4.5hr) after which treatment risk outweighed benefits. Now, we can see exactly which brain tissue is dead (red) vs salvageable (green). Some people's backup blood vessels keep tissue alive for 24hrs - we can spot and save them. CT Perfusion isn't just for strokes: — helps catch aggressive cancers — guides biopsies — finds blocked heart arteries — spots internal bleeding — checks if treatments work By tracking blood flow anywhere in the body, it saves lives in many ways. The tech industry rarely talks about breakthroughs in healthcare and medical imaging. CT Perfusion is just one such technology that combines hardware and software innovation to beat the clock in stroke care.

Title: Revolutionizing PET Imaging: The Power of Photon Entanglement Main Text: Did you know that every time a positron annihilation occurs in PET imaging, the two 511 keV photons produced are quantum entangled? In traditional PET, we detect coincidences based only on timing and position. But the deeper quantum reality tells us: these photons are also linked in their polarization states! Photon entanglement means that their properties are correlated, even across large distances. Recent research shows that by analyzing this entanglement: We can reject scattered and random events more effectively. We can enhance image contrast and resolution. We can lower patient radiation doses or reduce scan times. Quantum-Enhanced PET (QE-PET) could be the future — combining quantum physics and advanced detector technologies (like CZT detectors) to achieve cleaner, sharper, and faster PET imaging. Imagine a PET system that not only knows when two photons arrived… but also knows if they were "born together". The future of molecular imaging is not just about faster or higher resolution — it's about smarter physics. #PET #QuantumPhysics #MedicalImaging #MolecularImaging #PhotonEntanglement #HealthcareInnovation --- Infographic Points (to design below): 1. Title: PET Imaging & Photon Entanglement 2. What Happens in PET? Positron meets electron. Two 511 keV photons are emitted — entangled! 3. Traditional PET: Detects photons based on timing. Accepts some noise (scatter and randoms). 4. Quantum-Enhanced PET: Detects timing and polarization entanglement. Rejects scatter and randoms more precisely. 5. Benefits: Sharper images. Lower radiation dose. Shorter scanning time. 6. How it works: CZT detectors measure Compton scatter patterns. Quantum analysis confirms true annihilation events. 7. The Future: Combining quantum physics with AI-driven PET systems. Toward smarter, safer molecular imaging! https://lnkd.in/eshp7Kny

📌 Open-Source Medical Imaging AI Models (2024–2025) This curated list highlights the latest open-source AI models transforming medical imaging, from generalist vision-language foundations to specialized tools for segmentation, diagnosis, and report generation. Explore models across radiology, oncology, and multimodal analysis. Full links and details below. 👇 📌 Foundation & Multimodal Models • Rad-DINO – Self-supervised ViT trained on 1M+ chest X-rays • RayDINO – Large-scale DINO-based transformer for multi-task chest X-ray learning • Med-Gemini – Gemini-based model fine-tuned for multi-task chest X-ray applications • Merlin – Large 3D vision–language model for CT interpretation and reporting • RadFound – Radiology-wide VLM for report generation and question answering • LLaVA-Rad – Vision–language model for chest X-ray finding generation 📌 Segmentation Models • MedSAM2 – Promptable 3D segmentation model extending Segment Anything to medical imaging • FluoroSAM – SAM variant trained from scratch on synthetic X-ray/fluoro images • ONCOPILOT – Interactive model for CT-based 3D tumor segmentation in oncology 📌 Task-Specific / Tuned Models • MAIRA-2 – Enhanced CXR report generator with finding localization • CheXagent – Instruction-tuned multimodal model for chest X-ray tasks • RadVLM – Dialogue assistant for chest X-ray interpretation and reporting • Mammo-CLIP – CLIP-based model for mammogram classification and BI-RADS prediction • CheXFound – ViT model using GLoRI architecture for disease localization in X-rays Know a model that got missed? Drop it in the comments, let’s build this resource list together. 🤔 _________________________________________________ #ai #imaging #radiology #oncology #machinelearning

BLUsH: bioluminescence imaging using hemodynamics A groundbreaking advancement in neuroimaging has been achieved with the development of BLUSH, a technique that translates bioluminescence into MRI-detectable hemodynamic signals. This novel method overcomes the inherent limitations of traditional optical imaging in deep tissues, offering unprecedented spatial resolution and depth penetration for in vivo studies. By converting photon emission into localized vascular responses, BLUSH enables real-time visualization of biological processes with applications spanning from neural circuit mapping to tumor tracking. This technology holds immense potential to accelerate neuroscience research and clinical translation. BLUSH, with its ability to convert bioluminescence into MRI-detectable signals, opens up a vast array of potential applications in biomedical research: 1. Tracking Cellular Dynamics in Real-Time - Cancer research: Monitoring tumor growth, metastasis, and response to therapy. - Immunology: Studying immune cell trafficking and response to pathogens or inflammation. - Stem cell research: Tracking cell differentiation and migration in vivo. 2. Neurological Studies - Neural circuit mapping: Visualizing neural activity patterns in real-time. - Neurodegenerative diseases: Monitoring disease progression and therapeutic efficacy. - Brain tumors: Tracking tumor growth and response to treatment. 3. Drug Delivery and Pharmacokinetics - Monitoring drug distribution: Tracking the movement of drug-carrying nanoparticles or cells. - Assessing drug efficacy: Evaluating the impact of therapeutics on target tissues. 4. Developmental Biology - Embryonic development: Studying cell fate determination and organogenesis. - Regenerative medicine: Monitoring tissue regeneration and repair. Technical Challenges and Future Directions While BLUSH represents a significant advancement, there are still technical challenges to overcome: - Uniform photosensitization: Achieving consistent bPAC expression in blood vessels across different tissue types remains a challenge. - Spatial resolution: Further improvements in MRI resolution and image processing techniques are needed to enhance spatial accuracy. - Quantitative analysis: Developing quantitative methods to correlate BLUSH signals with bioluminescence intensity is essential for accurate data interpretation. Future research should focus on addressing these challenges, as well as exploring the potential of BLUSH in combination with other imaging modalities for multimodal analysis. By overcoming these limitations, BLUSH has the potential to revolutionize biomedical research and drug development. #neuroscience #biomedicalengineering #imaging #bioluminescence #MRI #research #neuroscience

Robotic surgery systems these days from companies such as Intuitive, Medtronic and CMR Surgical offer substantial imaging capabilities that assist surgeons making complex surgeries less complex, including the ability to visualize patient's anatomy in 3D, all in real-time. However, there is still a challenge in certain situations to image in realtime deep tissue structures. Luckily, with assistance from cutting-edge technologies, surgeons can see through tissues, not so much like x-ray vision, but tech is moving fast towards that! Here are some groundbreaking innovations making this possible: 1. Hyperspectral Imaging (HSI): This technique captures and processes information across a wide range of wavelengths, allowing for detailed visualization of tissues. 2. Near-Infrared Fluorescence (NIR-I and NIR-II) Imaging: These techniques use near-infrared light to penetrate deeper into tissues, offering high-resolution images that help surgeons identify and navigate through critical structures during surgery. 3. Photoacoustic Imaging: This innovative method combines laser-induced ultrasound with optical imaging to provide detailed images of deep tissues. It is particularly useful for visualizing blood vessels and detecting tumors. 4. Ultrasound-Controlled Fluorescence Imaging: By using ultrasound waves to activate fluorescent markers, this technology allows for precise imaging of deep tissues, enhancing the surgeon’s ability to target specific areas. These innovations are transforming the world of surgery, making procedures safer, faster, and more effective. A future in which surgeons will operate with real X-ray vision is not far. 🚀🔬 See some interesting links in the comment section. #Surgery #Innovation #Healthcare #DeepTissueImaging #XRayVision #roboticsurgery

Check out recently released healthcare foundation models from Microsoft: MedImageInsight - Embedding model for advanced image analysis, including classification and similarity search in medical imaging. - Streamlines workflows across radiology, pathology, ophthalmology, dermatology, and other modalities. - Researchers can use embeddings directly or build adapters for specific tasks. - Enables tools to automatically route imaging scans to specialists or flag potential abnormalities. - Enhances efficiency and patient outcomes. - Supports Responsible AI safeguards like out-of-distribution detection and drift monitoring to maintain stability and reliability. CXRReportGen - Multimodal AI model for generating detailed, structured reports from chest X-rays. - Incorporates current and prior images along with key patient information. - Highlights AI-generated findings directly on images to align with human-in-the-loop workflows. - Accelerates turnaround times while enhancing diagnostic precision. - Supports diagnosis of a wide range of conditions—from lung infections to heart problems. - Addresses the most common radiology procedure globally. MedImageParse - Precise image segmentation model covering X-rays, CT scans, MRIs, ultrasounds, dermatology images, and pathology slides. - Can be fine-tuned for specific applications like tumor segmentation or organ delineation. - Enables developers to build AI tools for sophisticated medical image analysis.